Paper Citation Record · LEDGER

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.12980.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.12980 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:39:20.997542Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact11
verified fuzzy2
unresolved24
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bdefc3d3-b44e-4376-93c4-effaa8e122ed · outbound

This paper cites Oliver Bott, Matthias Schrumpf, Jens Michaelis, and Torgrim Solstad.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Oliver Bott, Matthias Schrumpf, Jens Michaelis, and Torgrim Solstad

Reference 5

Resolution: verified exact; doi, observed 2026-08-10T16:39:21.115968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 84f7aebf-3a31-45dd-9900-511d4186eee6 · outbound

This paper cites To appear.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities To appear

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-10T16:39:22.095996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation df458ec2-5bd3-47df-96d0-55767a73be00 · outbound

This paper cites Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:39:21.977018Z

Source-reported events for the cited work

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Observation 1e647545-8262-40af-98f7-f92768973e90 · outbound

This paper cites Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks

Reference 10

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Observation 344ee66b-6890-4071-9bf9-d4fae444caf7 · outbound

This paper cites doi: 10.18653/v1/2023.findings-emnlp.868.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.18653/v1/2023.findings-emnlp.868

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T16:39:21.937252Z

Source-reported events for the cited work

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Observation 7127de50-eeb4-4f29-a942-91aaade09bd8 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities The Curious Case of Neural Text Degeneration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.876326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cac11b9-1153-458f-9256-883028a8a2d6 · outbound

This paper cites doi: 10.18653/v1/2024.starsem-1.34.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.18653/v1/2024.starsem-1.34

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.885105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27eaded-8653-4f30-ba7f-36794a0c5c92 · outbound

This paper cites John praised Mary because he? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities John praised Mary because he? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6c2979f7-eab2-4d68-8647-e8cd80a6cf25 · outbound

This paper cites Few-shot Learning with Multilingual Language Models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Few-shot Learning with Multilingual Language Models

Reference 24

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Source-reported events for the cited work

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Observation 54cfbd82-1e85-432b-87ce-122f3efeba6e · outbound

This paper cites Language Models as Models of Language.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Language Models as Models of Language

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.915210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61938eff-d773-441e-9142-4f8065c19730 · outbound

This paper cites doi: https://doi.org/10.1016/j.nlp.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: https://doi.org/10.1016/j.nlp

Reference 26

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Source-reported events for the cited work

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Observation b39cc43f-a87d-40bb-af75-27e5ebbbb513 · outbound

This paper cites A Thorough Examination of Decoding Methods in the Era of LLMs.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities A Thorough Examination of Decoding Methods in the Era of LLMs

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ded53c60-fcb5-4c15-a076-2a0c3413793d · outbound

This paper cites mGPT: Few-Shot Learners Go Multilingual.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities mGPT: Few-Shot Learners Go Multilingual

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.935910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8545ca0d-8e35-4d4d-9031-890993b1554d · outbound

This paper cites Torgrim Solstad and Oliver Bott.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Torgrim Solstad and Oliver Bott

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.940830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 747aad37-2777-404c-8cbe-21ed100f13a6 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.945427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd9d3cf3-c911-4ca1-84d7-2050a9879f87 · outbound

This paper cites doi: 10.18653/v1/P19-1164.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.18653/v1/P19-1164

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.950770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2600b93-1576-4f3c-aefb-17fd237e13b6 · outbound

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Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Unresolved cited work

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4ede366b-4ace-4c11-9e7b-2e1e47dc696e · outbound

This paper cites Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.964602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adddd6be-bf94-4888-a973-6e1ea6f15cab · outbound

This paper cites doi: 10.18653/v1/2020.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.18653/v1/2020

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.974005Z

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Unavailable: canonical work link unavailable.

Observation 535577ab-8ff4-4e31-ab3b-51eac3d8bec7 · outbound

This paper cites doi: https: //doi.org/10.1016/j.cognition.2021.104759.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: https: //doi.org/10.1016/j.cognition.2021.104759

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.978179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf24acc0-9070-4db6-9562-bbaab73a114b · outbound

This paper cites Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.982942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40dac65c-bc31-42ad-9c8f-b0632fef3833 · outbound

This paper cites How well do Large Language Models perform in Arithmetic tasks?.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities How well do Large Language Models perform in Arithmetic tasks?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T16:39:20.988290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2446880a-239e-457b-a526-2626d96ff987 · outbound

This paper cites This isn’t the bias you’re looking for: Implicit causality, names and gender in german language models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities This isn’t the bias you’re looking for: Implicit causality, names and gender in german language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:39:22.135108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13584d47-890a-460c-b65e-bf12de8bf727 · outbound

This paper cites a survey on GPT-3.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities a survey on GPT-3

Reference 42

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Unavailable: canonical work link unavailable.

Observation 31ca8c5f-1e59-46ae-a025-207bb052ed38 · outbound

This paper cites Ronen Eldan and Yuanzhi Li.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Ronen Eldan and Yuanzhi Li

Reference 1980

Resolution: verified exact; doi, observed 2026-08-10T16:39:21.096235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c3c07044-8cba-44d2-8bf5-3d1b946be3a3 · outbound

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Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Unresolved cited work

Reference 1990

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 90defa58-28f6-4ff1-97bd-aba168790fa9 · outbound

This paper cites URL http://dx.doi.org/10.1075/hcp.8.04ari.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities URL http://dx.doi.org/10.1075/hcp.8.04ari

Reference 2001

Resolution: verified exact; doi, observed 2026-08-10T16:39:21.132395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 001dcc56-f219-4e0a-b259-46d77cd69a26 · outbound

This paper cites Discourse structure interacts with reference but not syntax in neural language models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Discourse structure interacts with reference but not syntax in neural language models

Reference 2006

Resolution: malformed identifier; local_arxiv, observed 2026-08-10T16:39:22.000877Z

Source-reported events for the cited work

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Observation 90cfacb5-8636-49cc-9fb3-bcad7f61a721 · outbound

This paper cites Assessing BERT's Syntactic Abilities.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Assessing BERT's Syntactic Abilities

Reference 2008

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Source-reported events for the cited work

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Observation 6c33a0c4-eb61-486d-a2f5-e15226865645 · outbound

This paper cites doi: 10.1016/j.jml.2009.09.001.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.1016/j.jml.2009.09.001

Reference 2010

Resolution: verified exact; doi, observed 2026-08-10T16:39:21.066492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ffc47ccd-d645-4712-89de-830d02e3982d · outbound

This paper cites doi: 10.3758/s13428-010-0023-2.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.3758/s13428-010-0023-2

Reference 2011

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c121876-6e4a-46ea-8a2d-ef20fe867b9c · outbound

This paper cites Implicitness of discourse relations.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Implicitness of discourse relations

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:39:22.219617Z

Source-reported events for the cited work

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Observation f836e792-4628-41d0-ab10-7ea8a129c646 · outbound

This paper cites Robert D.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Robert D

Reference 2015

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Source-reported events for the cited work

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Observation d4f99604-b139-4d7c-8f66-012e8909a08c · outbound

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Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Unresolved cited work

Reference 2016

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Source-reported events for the cited work

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Observation 5a90f6b8-2cfe-4b9c-8229-50d2810b1aa2 · outbound

This paper cites Towards Reasoning in Large Language Models: A Survey.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Towards Reasoning in Large Language Models: A Survey

Reference 2017

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Observation 3f09c501-65bc-48f5-877d-dd244b5aade5 · outbound

This paper cites an unresolved cited work.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Unresolved cited work

Reference 2019

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Source-reported events for the cited work

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Observation 25c264a6-0a52-47d5-9a53-d7a9bf002cc8 · outbound

This paper cites doi: 10.18653/v1/2020.coling-main.107.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities doi: 10.18653/v1/2020.coling-main.107

Reference 2020

Resolution: verified exact; doi, observed 2026-08-10T16:39:21.149982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3bfebf8d-203d-47db-bea3-2d1bb1b32492 · outbound

This paper cites Dynabench: Rethinking Benchmarking in NLP.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Dynabench: Rethinking Benchmarking in NLP

Reference 2021

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Source-reported events for the cited work

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Observation 62b4d333-0724-45c4-a67d-6c458afe038f · outbound

This paper cites Sorting through the noise: Testing robustness of information processing in pre-trained language models.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Sorting through the noise: Testing robustness of information processing in pre-trained language models

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:39:21.557223Z

Source-reported events for the cited work

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Observation 9ac71b95-8611-4a82-8356-295251289812 · outbound

This paper cites Deep RNNs Encode Soft Hierarchical Syntax.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities Deep RNNs Encode Soft Hierarchical Syntax

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:39:22.118554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d9a1476d-6394-44ce-b20a-6489955bcd70 · outbound

This paper cites WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case.

Implicit Causality-biases in humans and LLMs as a tool for benchmarking LLM discourse capabilities WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:39:21.854376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.